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HYPOTHESIS AND THEORY ARTICLEpublished: 05 September 2013doi:
10.3389/fmicb.2013.00265
Controls on soil microbial community stability underclimate
changeFranciska T. de Vries1* and Ashley Shade2
1 Faculty of Life Sciences, The University of Manchester,
Manchester, UK2 Department of Molecular, Cellular, and
Developmental Biology, Yale University, New Haven, CT, USA
Edited by:Johannes Rousk, Lund University,Sweden
Reviewed by:Romain L. Barnard, Institut Nationalde la Recherche
Agronomique,FranceJennifer Talbot, Stanford University,USA
*Correspondence:Franciska T. De Vries, Faculty of LifeSciences,
University of Manchester,Michael Smith Building, OxfordRoad,
Manchester, M13 9PT, UKe-mail:
[email protected]
Soil microbial communities are intricately linked to ecosystem
functioning because theyplay important roles in carbon and nitrogen
cycling. Still, we know little about how soilmicrobial communities
will be affected by disturbances expected with climate change.
Thisis a significant gap in understanding, as the stability of
microbial communities, defined asa communitys ability to resist and
recover from disturbances, likely has consequencesfor ecosystem
function. Here, we propose a framework for predicting a
communitysresponse to climate change, based on specific functional
traits present in the community,the relative dominance of r- and
K-strategists, and the soil environment. We hypothesizethat the
relative abundance of r- and K-strategists will inform about a
communitysresistance and resilience to climate change associated
disturbances. We also proposethat other factors specific to soils,
such as moisture content and the presence of plants,may enhance a
communitys resilience. For example, recent evidence suggests
microbialgrazers, resource availability, and plant roots each
impact on microbial community stability.We explore these hypotheses
by offering three vignettes of published data that were-analyzed.
Our results show that community measures of the relative abundance
ofr- and K-strategists, as well as environmental properties like
resource availability andthe abundance and diversity of higher
trophic levels, can contribute to explaining theresponse of
microbial community composition to climate change-related
disturbances.However, further investigation and experimental
validation is necessary to directly testthese hypotheses across a
wide range of soil ecosystems.
Keywords: disturbance, drought, fungi, bacteria, PLFA,
pyrosequencing, resistance, resilience
INTRODUCTIONSoil microbial communities are intricately linked to
ecosys-
tem functioning because they play important roles in carbon(C)
and nitrogen (N) cycling, and feed back to plant communi-ties as
mutualists and pathogens (Van der Heijden et al., 2008).Although
much research has been done to study the impactsof a range of
disturbances on soil microbial communities andtheir functioning
(Griffiths and Philippot, 2013), many uncer-tainties remain about
the controls on soil microbial communitystability (Box 1), and the
consequences of disturbance-inducedchanges in microbial communities
for their capacity to withstandfurther disturbances. This may be in
part because most stud-ies measured the stability of bulk microbial
properties, such asbiomass and respiration, rather than of
community structure (thenumber of different taxa and their relative
abundances; Box 1).However, changes in the abundances or relative
contributionsof community members may have implications for the
stabil-ity of a microbial community, and these kinds of
membershipchanges may not be apparent when measuring bulk
microbialproperties. In addition, soils are unique and highly
heterogeneousenvironments, and controls on microbial community
stability insoil might differ from other systems. We argue that
knowledge
on what controls soil microbial community stability is
pivotalfor predicting the impacts of climate change on soil
microbialcommunities and the processes that they drive.
Here, drawing from findings from both terrestrial and
aquaticsystems, we formulate hypotheses on the controls of
resistanceand resilience of microbial communities in soil, focusing
ondisturbances associated with climate change (Box 1).
Climatechange is expected to result in increased frequency of
droughtand heavy rainfall, increases in temperature, and increased
litterinputs and plant root exudates through elevated
concentrationsof atmospheric CO2, which all have significant
impacts on soilmicrobial community structure and functioning
(Bardgett et al.,2013). Here, we focus on pulse disturbances
associated with cli-mate change, such as drought, increased
rainfall, and increasedlitter inputs, because the clear start and
end point of these dis-turbances allows for assessing both
resistance and resilience ofmicrobial community composition (Box
1). We use three casestudies in which we re-analyze published data
on the impact ofthese disturbances on microbial communities to
further developour proposed hypotheses. Finally, we synthesize our
findings, andrecommend ways of testing our hypotheses about
controls of soilmicrobial community stability.
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de Vries and Shade Controls on microbial community stability
Box 1 | Glossary
Microbial community composition: the assortment of microbialtaxa
that comprises a community (Hunter, 1990).Microbial community
structure: the membership and (relative)abundances of microbial
taxa in a community (Anderson et al.,2011).Trait: phenotypic
characteristic or attribute of an individualmicrobe that is
affected by genotype and the environment(Campbell and Reece,
2006).Functional trait: trait with a direct functional role that
defines amicrobe in terms of its ecological role, i.e., its
interaction withother microbes and its environment (Lavorel and
Garnier, 2002;Wallenstein and Hall, 2012).Disturbance: causal event
that alters a community directly orindirectly, typically through
its effect on the communitys envi-ronment (Rykiel, 1985; Glasby and
Underwood, 1996).Pulse disturbance: relatively discrete (with a
clear start and endpoint), short-term events with a clear start and
end point (Lake,2000).Press disturbance: long term event or
continuous change (Lake,2000).Climate change: statistically
significant variation in the meanstate of the climate or its
variability, caused either by nat-ural internal processes or
external forcing, or by persistentanthropogenic-induced changes in
the composition of theatmosphere or land use (IPCC, 2007). Here, we
focus ondisturbances associated with climate change that are
rele-vant to soil communities and processes, namely
elevatedatmospheric CO2 and its indirect effects (increased soil
Cinputs through roots, root exudates, and increased litter
fall),extreme weather events (drought and heavy rainfall),
andwarming.Global change: changes in the global environment that
mayalter the capacity of the Earth to sustain life
(Schlesinger,2006), including both land-use and climate change.
Here,we focus on global change disturbances such as land usechange
and N deposition rather than on climate
changedisturbances.Stability : the tendency of a community to
return to amean condi-tion after a disturbance (Pimm, 1984);
includes the componentsof resistance and resilience (see also Worm
and Duffy, 2003;Shade et al., 2012a).Resistance: the ability of a
community property or process toremain unchanged in the face of a
specific disturbance (Pimm,1984; Allison and Martiny,
2008).Resilience: the ability of a community property or process
torecover after a specific disturbance, often reported as a rate
ofreturn (Allison and Martiny, 2008).Adaptation: the process
through which a microbe increases itsfitness in a particular
environment (Wallenstein and Hall, 2012),i.e., optimization of
traits that increase fitness.Evolutionary adaptation: changes in
the relative abundance ofgene frequencies in a gene pool to
optimize traits that increasefitness as a result of changes in
environmental conditions(Campbell and Reece, 2006; Orsini et al.,
2013).
MICROBIAL COMMUNITY STRUCTURE, SPECIFIC TRAITSPRESENT IN A
COMMUNITY, AND THE R-K SPECTRUMMuch work has been done on the
relationship between the diver-sity and structure of microbial
communities and their responseto disturbance, often with
contrasting results. Most evidence
for relationships between microbial communities and
stability(resistance or resilience under disturbance) comes from
aquaticmicrocosm studies (e.g., Wertz et al., 2007; Wittebolle et
al.,2009; Eisenhauer et al., 2012). The majority of these studies
havefocused on the stability of processes or bulk microbial
proper-ties (e.g., biomass or functioning) under disturbance,
rather thanthe stability of community structure itself. Disturbance
influencesmicrobial community structure if species differ in their
trade-offbetween growth rate and disturbance tolerance (Engelmoer
andRozen, 2009). Therefore, specific functional traits (Box 1) may
bemore informative of community stability in disturbed
ecosystemsthan community composition and structure (Lennon et al.,
2012;Wallenstein and Hall, 2012; Mouillot et al., 2013). For
example,the ability to resist dehydration via synthesis of the
sugar tre-halose to maintain cell membrane integrity (e.g.,
McIntyre et al.,2007; Zhang and Van, 2012) may be an important soil
micro-bial trait to consider for drought resistance, whereas the
abilityto use specific C or N forms that are released when a
droughtends might inform about resilience (Borken and Matzner,
2009)(Table 1). In contrast, more general stress-response
pathways,such as the sporulation pathway of Bacillus subtilis
(e.g., Higginsand Dworkin, 2012) may be universally useful for
maintainingstability in the face of a variety of disturbances.
Dispersal mechanisms and connectivity are important forthe
resilience of microbial communities because the success ofregional
dispersal affects the maintenance of local diversity
(e.g.,Matthiessen et al., 2010; Lindstrom and Langenheder,
2012).Connectedness of metapopulations has been shown to be
animportant factor in the response of aquatic communities to
dis-turbance (e.g., Altermatt et al., 2011; Carrara et al., 2012),
butsuch evidence is lacking for soils. Dispersal mechanisms are
likelyto play an even more important role for the recovery of
micro-bial communities in soil because of its heterogeneous
nature(Ritz et al., 2004), and low moisture content can hamper
dis-persal of soil microbes by spatially isolating
metacommunities(Treves et al., 2003). However, soil microbes can
also disperse viaaboveground mechanisms. For example, fungi that
rely on activedispersal through airborne spores (e.g., Roper et
al., 2010) mayhave greater resilience than bacteria that lack more
active disper-sal mechanisms (Kasel et al., 2008; but see
Barcenas-Moreno et al.,2011). On the other hand, bacteria, archaea,
and phytoplanktoncells are thought to passively disperse easily
because of their largepopulations and small body sizes (e.g.,
Baas-Becking, 1934; Finlayand Clarke, 1999).
From the above, we infer that specific microbial traits
arepivotal for determining microbial community response to
distur-bance, and that the ability of a microbial community to
resist orrecover from a specific disturbance may be informed by the
dom-inance, or community-weighted mean, of a specific
functionaltrait (e.g., Wallenstein and Hall, 2012) (Table 1).
Recent advancesin sequence-based metagenomics allow for
identification of func-tional genes in a microbial community
(Thomas et al., 2012).However, although the presence and expression
of specific func-tional genes in soil microbial communities has
been shown torespond to global change and climate change
disturbances (e.g.,Baldrian et al., 2012; Yergeau et al., 2012;
Yarwood et al., 2013),the relative abundance of functional genes
has never been used
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de Vries and Shade Controls on microbial community stability
Table 1 | Examples of microbial traits and the genes involved
that might play a role in the resistance and resilience of
microbial communities
to climate change.
Trait Genes involved Process Climate change driver
References
Desiccation and heatresistance
otsBA, otsA Trehalose synthesis Capsule Drought, warming Canovas
et al., 2001; McIntyre et al.,2007; Miller and Ingram,
2008;Mordhorst et al., 2009; Zhang andVan, 2012
neuO O- acetylation
Sporulation >500 Multiple Wide range of disturbances Higgins
and Dworkin, 2012
Use of specific N forms amoA Ammonia oxidation Increased
nitrogen availabilitythrough warming andrewetting after
drought,changes in dominant N formsthrough warming, changes insoil
moisture, and changes insoil C availability throughelevated CO2
Lamb et al., 2011; Long et al., 2012;Yergeau et al., 2012;
Yarwood et al.,2013
cnorB Nitric oxide reduction
nosZ Nitrous oxide reduction
narG Nitrate reduction
nirK, nirS Nitrite reduction
nifH Nitrogen fixation
Use of specific C forms chiA Chitin degradation Changes in soil
C availabilitythrough rewetting afterdrought, and elevated CO2
Theuerl and Buscot, 2010; Theuerlet al., 2010; Edwards et al.,
2011;Baldrian et al., 2012; Castro et al.,2012; Nannipieri et al.,
2012
mcrA Methanogenesis
pmoA Methane oxidation
gtlA Citrate synthesis
cbhI Cellulose degradation
lcc Lignin and phenol oxidation
glu Glucose oxidation
to infer a communitys ability to withstand and recover from
dis-turbances. This approach still has many caveats; newly
discoveredgene sequences often lack homology to known genes in
currentdatabases and remain unknown until biochemical
characteriza-tion and annotation of their functional abilities, and
microorgan-isms may carry the genetic capacity to exhibit a certain
functionaltrait, but, ultimately, not express the gene or produce
an activegene product in nature. Thus, to capitalize on
sequence-basedmetagenomic tools for the understanding of functional
traits, thetraits of interest and their genes and regulatory
pathways must bewell-characterized.
In addition to specific traits, microorganisms can be
character-ized according to their life-history strategy:
r-strategists (termedruderals in plant ecology, and copiotrophs in
microbial ecol-ogy) have high growth rates and low resource use
efficiency,and K-strategists (termed competitors in plant ecology,
and olig-otrophs in microbial ecology) have low growth rates and
highresource use efficiency (Klappenbach et al., 2000; Fierer et
al.,2007). This assumed fundamental trade-off between growth
rateand resource use efficiency (Hall et al., 2009) may underlie
thecapacity of microbial communities to respond to
disturbance(Schimel et al., 2007; Wallenstein and Hall, 2012), as
commu-nity structure will change if the taxa present differ in this
trade-off(Engelmoer and Rozen, 2009). There is evidence from both
plantand soil communities that K-strategists aremore resistant, but
lessresilient, to climate change-related disturbances than
r-strategists(Grime, 2001; Haddad et al., 2008; Bapiri et al.,
2010; De Vrieset al., 2012a; Lennon et al., 2012), and a trade-off
between resis-tance and resilience is widely documented (Pimm,
1984; Hedlundet al., 2004; De Vries et al., 2012a). Different soils
with different
microbial communities have been compared in their responseto
disturbances (mostly in terms of bulk biomass and func-tion), and
changes in the abundances or relative contributionsof community
members have been linked to the overarching sta-bility of the
microbial community structure itself (Griffiths andPhilippot,
2013). As some taxa may be more sensitive to certaindisturbances
than other taxa, it is possible that their differentialresponses
impact not only the abundances of insensitive commu-nity members
(for instance, through changes in the strengths ofmicrobial
interactions, such as the release of an insensitive taxonfrom
competition due to the decrease in abundance of a taxonsensitive to
disturbance), but also the overarching resistance andresilience of
the community. Here, we propose that community-level measures that
have a theoretical relationship with a specificfunctional trait, or
with the r-K-strategist spectrum, might pre-dict the response of
soil microbial community structure to pulsedisturbances associated
with climate change.
HYPOTHESIS 1: THE RESISTANCE OF MICROBIAL COMMUNITYSTRUCTURE TO
DISTURBANCE INCREASES WITH INCREASINGRELATIVE ABUNDANCE OF K
STRATEGISTS (OR OLIGOTROPHS), BUTTHE RESILIENCE
DECREASES.Gram-positive bacteria often are slower growing than
Gram-negative bacteria (Prescott et al., 1996), and therefore the
ratiobetween Gram-positives and Gram-negatives of a soil
microbialcommunity might be indicative of the prevalence of
K-strategistsin that community. In addition, the ability ofmany
Gram-positivebacteria to sporulate allows them to withstand a
variety of dis-turbances, including drought (Drenovsky et al.,
2010; Higginsand Dworkin, 2012). Therefore, we propose that the
resistance
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de Vries and Shade Controls on microbial community stability
of microbial community structure will increase with
increasingGram-positive/Gram-negative ratio, or increasing relative
abun-dance of Gram-positive bacteria.
Similarly, microbial communities that have a high proportionof
fungi compared to bacteria are associated with nutrient [N
andphosphorus (P)] poor conditions that require high resource
useefficiency, and fungi typically are considered to be slower
grow-ing than bacteria (Six et al., 2006). Therefore, we argue that
thefungal/bacterial ratio of a soil microbial community may also
beindicative of the prevalence of K-strategists in that
community,and, following this, the resistance of microbial
community struc-ture will increase with increasing
fungi-to-bacteria (F/B) ratio,or increasing relative abundance of
fungi, whereas the resiliencewill decrease. The carbon-to-nitrogen
(C/N) ratio of microbialcommunities may be also be linked to
intrinsic growth rate; fungiare slower-growing and have wider C/N
ratios than bacteria (VanVeen and Paul, 1979; Bloem et al., 1997;
but see Cleveland andLiptzin, 2007), thus, microbial communities
that are dominatedby fungi rather than bacteria will have a wider
C/N ratio.
Finally, the resilience of microbial community structure
willincrease with increasing abundance of bacteria that can
beclassified as copiotrophs, such as many members of the
-proteobacteria and Bacteriodetes, and decreasing abundance
ofoligotrophs, such as many members of the Acidobacteria (Fiereret
al., 2007). Notably, many oligotrophic microorganisms may
ber-strategists, while many copiotrophic microorganisms may alsobe
K-strategists, and so there is likely overlap between the twotypes
of classification. Although we propose here that the abovecommunity
attributes can be used to predict the resistance andresilience of
microbial community composition, we acknowledgethat within the
categories and distinctions we propose, there willof course be
exceptions that do not respond as we suggest.
At first, it may seem circular that quickly-growing
organismswill be less resistant but more resilient to disturbances,
and thatcommunities with frequent disturbance regimes may be
domi-nated by microorganisms exhibiting these strategies because
ofselection. However, we believe that our hypothesis is not
merelyself-affirming because microorganisms may respond to
distur-bances not only by growing and dying, but also, for
example,by temporarily changing their physiological state or
metabolism(e.g., entering dormancy), maintaining stochastic gene
expres-sion, exhibiting phenotypic plasticity, or being rescued by
dis-persal from nearby meta-communities (e.g., Shade et al.,
2012a).Therefore, given the array of complex responses that
microorgan-isms may have when challenged with a disturbance, growth
is notthe only mechanism that could maintain community
stability.
HIGHER TROPHIC LEVELSAlthough there is some evidence from
aquatic and terrestrialstudies that the presence of higher trophic
levels can enhancethe recovery of microbial biomass and activity
(Maraun et al.,1998; Downing and Leibold, 2010), almost no
attention hasbeen given to the role of higher trophic levels of the
soil foodweb in controlling resilience of microbial community
structure.Microbial grazers have the potential to affect resilience
of micro-bial community structure via two mechanisms. First, they
canaid the dispersal of microbes by carrying them in their guts
or
on their surfaces. For example, bacterial-feeding nematodes
dis-perse bacteria by carrying them both their surfaces and in
theirguts (Ingham, 1999), fungal spores are dispersed by the
move-ment of fungal grazers such as collembolans (Renker et al.,
2005),and bacterioplankton may hitchhike on zooplankton carapacesto
overcome otherwise impenetrable gradients in water columns(Grossart
et al., 2010). In addition, microbial grazers affect micro-bial
communities by preferentially feeding on specific taxa orfunctional
groups, thereby either reducing their abundance orstimulating their
turnover and activity (Chen and Ferris, 2000;Cole et al., 2004; Fu
et al., 2005; Postma-Blaauw et al., 2005). As anexample,
heterotrophic nanoflagellates, prominent bacteriovoresin aquatic
systems, often preferentially graze on
medium-sizedbacterioplankton, leaving the small and large-bodied
organismsbehind (Miki and Jacquet, 2008).
HYPOTHESIS 2: THE RESILIENCE OF MICROBIAL COMMUNITYSTRUCTURE
INCREASES WITH GREATER DIVERSITY OF ORGANISMSOF HIGHER TROPHIC
LEVELSDifferent microbial grazers have different feeding
preferences,and different soil faunal species often have different
move-ment patterns. Thus, we hypothesize that a greater diversity
orspecies richness of higher trophic levels in the soil food
webenhances resilience of soil microbial communities after
distur-bance, because they stimulate the growth and dispersal of a
widerrange of soil microbes than faunal communities of lower
diversity.
RESOURCE AVAILABILITYAs suggested by Wallenstein and Hall (2012)
resource availabilitymight constrain the rate of soil microbial
community adaptationand recovery; in low resource environments,
shifts in microbialcommunity structure will be slow, whereas in
high resource envi-ronments, communities will respond rapidly.
Indeed, resourceavailability has been linked to resilience of
microbial and fau-nal biomass several times (Orwin et al., 2006; De
Vries et al.,2012b). It was observed (but not quantified in regards
to com-munity composition) that the resilience of both microbial
andfaunal communities seemed to be increased by the presence
ofplants (De Vries et al., 2012b) presumably because plants
offersubstantial belowground carbon inputs for microbial
communi-ties. Resource availability has the potential to both
enhance andretard microbial community resilience, depending on the
remain-ing microbial traits after a disturbance: low resource
availabilitymay give slow-growing (oligotrophic) microbes a
competitiveadvantage, whereas high resource availability may favor
fast-growing (copiotrophic) microbes. Therefore, we propose that
agreater resource availability, diversity, and heterogeneity
wouldincrease community resilience after a disturbance, and
indeed,several studies report a positive effect of plant species
diversity(with presumably a diversity of belowground root exudates
andlitter inputs) on the stability of microbial biomass and
micro-bial processes (Milcu et al., 2010; Royer-Tardif et al.,
2010).Moreover, root exudates form a tight evolutionary link
betweenplants and microbial communities (Badri and Vivanco,
2009),and recent evidence showed that different chemical
composi-tions of Arabidopsis root exudates select for different
microbialcommunities (Badri et al., 2013), thereby potentially
affecting the
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response of those communities to climate change. Because
plantsrespond to climate change by modifying their C balance
(Atkinand Tjoelker, 2003; Chaves et al., 2003), temporal changes
inroot exudation especially have great potential to affect
microbialcommunity responses to climate change.
HYPOTHESIS 3: THE RESILIENCE OF MICROBIAL COMMUNITYSTRUCTURE
INCREASES WITH GREATER RESOURCE AVAILABILITY.BECAUSE OF THE
BELOWGROUND C INPUTS BY PLANT, THEPRESENCE OF A PLANT WILL INCREASE
THE RESILIENCE OF THEMICROBIAL COMMUNITYIncreased concentrations of
labile carbon, nitrogen, and phos-phorus as a result of greater
resource availability might allowmicrobial taxa to maximize their
intrinsic growth rate and thusincreases the resilience of microbial
community composition.We also hypothesize that the presence of a
plant enhances theresilience of microbial community structure
through its below-ground carbon inputs.
MOISTURE AVAILABILITYMoisture availability plays a crucial role
for microbial activity andsurvival, because microbes are in close
contact with water andhave semi-permeable cell walls. In addition
and as briefly men-tioned earlier, low soil moisture content limits
the dispersal ofmicroorganisms (Carson et al., 2010; Kravchenko et
al., 2013).However, moisture is also limiting for the movement of
microbialgrazers such as nematodes (Young et al., 1998), which, as
hypoth-esized above, might promote growth and dispersal of
microbesand increase microbial community resilience.
HYPOTHESIS 4: MOISTURE AVAILABILITY INCREASES RESILIENCE
OFMICROBIAL COMMUNITY STRUCTUREWe hypothesize that relatively
higher moisture availabilityincreases the recovery of microbial
community structure afterdrought, and also after other types of
disturbance, such as changesin N and C availability (as a result of
increased atmospheric CO2concentrations) or heat waves.
METHODSWe analyzed three case studies to test the hypotheses
about soilmicrobial community resistance and resilience outlined
above,focusing on drought, rainfall, and increased litter inputs.
In allthree case studies, we calculated Bray-Curtis similarities
betweendisturbed and control microbial communities as a measure
ofboth resistance and resilience of microbial community
structure.For resistance, this was the similarity between the
disturbed treat-ment and the control at the end of the disturbance;
for resilience,it was the similarity between the disturbed
treatment and thecontrol after ending the disturbance. In both
cases, a similar-ity of 1 would mean maximum resistance (no effect
of distur-bance) or resilience (complete recovery). We used axis
scores fromordination plots as metrics of microbial community
structure,as well as F/B ratio and Gram-positive/Gram-negative
ratio. Wefitted single-variable linear and non-linear models
[including aquadratic term of the significant explanatory
variable(s)] (lmfunction in R) to explain resistance and resilience
from met-rics of microbial community structure, as well as from
higher
trophic level richness and numbers, soil C and N
availability,and soil moisture content. If the quadratic term was
signifi-cant, we performed an ANOVA to test whether the
non-linearmodel significantly improved model fit. Finally, we
fitted the bestexplaining additive model for microbial community
resistanceand resilience using parameters that had shown to be
significantin the single-variable models. All analyses were
performed in R[version 2.15.2, (2012)].
CASE STUDY 1: RESPONSES OF GRASSLAND ANDWHEATFIELD MICROBIAL
COMMUNITIES TO MULTIPLE DROUGHTEVENTSThe data from case study 1
were originally published in twopapers: De Vries et al. (2012a) and
De Vries et al. (2012b). Theexperiment investigated the responses
of the entire soil food weband of C and N cycling in grassland and
wheat soil to drought.The experiment included two phases: a
field-based drought anda glasshouse-based drought. During the
glasshouse-based experi-ment, the response of biomass of functional
groups and processesin both control and drought treatments was
monitored directly1, 3, 10, and 77 days after ending the drought.
This, in combi-nation with 32 experimental units (land use field
drought glasshouse drought 4 replicates) per sampling, and an extra
setof pots in which a wheat plant was grown to assess the impact
ofplant presence on the recovery of the soil food web, resulted in
atotal of 192 observations. Microbial communities were
analyzedusing analysis of phospholipid-derived fatty acid profiles
(PLFA).In addition, soil concentrations of available C, N, and
moisturewere measured, as well as leaching and gaseous losses of C
andN. For more details on methods and experimental set up see
DeVries et al. (2012a,b).
The original publications focused on the impact of drought
onbiomass and activity of soil food webs, with only a minor role
forchanges in community composition. The biomass and activity
offungal-based soil food webs of grasslands were found to be
moreresistant to drought, whereas biomass and activity
bacterial-basedsoil food webs were more resilient. In addition, the
presence of aplant increased the resilience of microbial biomass,
and resilienceof microbial biomass was positively related to C
availability.Here, we re-analyzed microbial community data to test
our fourhypotheses about resistance and resilience of microbial
commu-nity structure. We calculated F/B ratio (the ratio between
thefungal PLFA 18:26 and the bacterial PLFAs i-15:0, a-15:0,
15:0,i-16:0, 16:17, 17:0, a-17:0, cyclo-17:0, 18:17, and
cyclo-19:0),Gram-positive/Gram-negative ratio (the ratio between
Gram-positive PLFAs i-15:0 and i-17:0 and Gram-negative PLFAs
a-C15:0, 16:17, cyclo-17:0, and cyclo-19:0) and PCA scores
ofrelative abundances of PLFAs [widely used in ecology for
analyz-ing PLFA profiles, e.g., in De Vries et al. (2012c)].
We found that both the resistance and the resilience of
micro-bial communities were explained by community structure. In
linewith hypothesis 1, resistance decreased with greater PC1
scores,along which Gram-negative abundance increased (Table A4),and
increased with greater Gram positive/Gram negative ratio(quadratic
relationship, Table 2). However, resistance decreasedwith greater
F/B ratio, which is in contrast with hypothesis 1,and with earlier
findings that resistance of biomass and activity
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de Vries and Shade Controls on microbial community stability
to drought increased with greater relative abundance of
fungi(Bapiri et al., 2010; De Vries et al., 2012a). A possible
expla-nation for this is that there is only one PLFA that
representsfungi, whereas there are ten PLFAs for bacteria. Thus,
changesin microbial community structure therefore are dominated
bychanges in the bacterial members, and the ratio between fun-gal
and bacterial PLFA might not be the most informative forthose
changes. In addition, the bacterial community in a fungal-dominated
microbial community might undergo more dramaticshifts in
composition because of intense competition with fungi.
In contrast to hypothesis 1, resilience decreased with
greaterPC1 scores, whereas it increased with greater C/N ratio of
micro-bial biomass and greater F/B ratio (included in best
model,Table 3) and Gram positive/Gram negative ratio (Table 3).
Thepositive relationship between resilience and F/B ratio as well
asGram-positive/Gram-negative ratio might reflect the fact that
theinitial changes in communities dominated by fungi and
Gram-positives were smaller and thus these remained more similar
totheir undisturbed counterparts throughout. This is further
sup-ported by the lack of evidence for a trade-off between
resistanceand resilience. In comparison, the resilience index
proposed byOrwin et al. (2010) calculates the resilience relative
to the initialchange in a parameter, and thus a low resistance is
more likelyfollowed by a high resilience. It goes beyond the scope
of thispaper to compare the use of different resilience indices,
but it isnoteworthy that different methods of calculating these
indices cangive different results.
Our results partly support hypotheses 2, 3, and 4. As
hypoth-esized, resilience of microbial community structure
increasedwith greater microarthropod richness. However, it
decreasedwith greater protozoa numbers (Table 3). When only the
lastsampling (77 days after ending the drought) was analyzed,the
positive relationship of resilience with greater microarthro-pod
richness was also significant (adjusted R-squared = 0.30,P =
0.017), but resilience increased with protozoa numbers(adjusted
R-squared= 0.22, P = 0.037). Notably, the presence ofa plant
strongly increased overall microbial community resilience,although
within land use and field drought treatments this effectwas not, or
only marginally, significant (Figure 1). Within theplant treatment,
resilience increased with increasing soil dissolvedorganic C
availability (adjusted R-squared = 0.22, P = 0.038).These results
support our hypothesis that plant belowground Cinputs increase
microbial community resilience. However, thelack of explanatory
power of overall resource availability for com-munity resilience
might indicate that other mechanisms are moreimportant, such as the
greater abundance of higher trophic levelsin plant treatments (De
Vries et al., 2012b), or plant impacts onsoil structure and
aeration, which were not measured here.
CASE STUDY 2: RESPONSE OF MICROBIAL COMMUNITIESFROM
INTENSIVELYMANAGED AND EXTENSIVELYMANAGED GRASSLAND TO DROUGHTIn
the study published by Gordon et al. (2008) the impactof a
glasshouse-based drought was assessed on microbial
Table 2 | Case study 1: regression models explaining microbial
community resistance to the glasshouse-based drought.
Model Intercept P Independent variablesincluded in model
Parameter value P Adj. R2
Single, linear 0.93
-
de Vries and Shade Controls on microbial community stability
communities from extensively managed, unfertilized, species
richgrassland, and from intensively managed, fertilized, and
heavilygrazed grassland, alongside measurements of C and N
leaching.The response of microbial biomass C and N, and C and N
leach-ing, was measured 1, 3, 9, 16, 30, and 50 days after
rewetting,while microbial community structure (as PLFAs) was
measuredonly at day 30. With two land uses, a drought vs. a
control,and four replicates, this resulted in 16 observations for
microbialcommunity structure.
In the original publication, the authors found that biomass Nof
the (fungal-dominated) microbial community of extensivelymanaged
grassland was less affected by drought than that of
thebacterial-dominated microbial community of intensively man-aged
grassland. Moreover, this was paralleled by smaller leachinglosses
of C and N from the grassland soil. Changes in microbialcommunity
composition were not analyzed quantitatively. Here,we re-analyzed
microbial community data to test our hypothe-ses that microbial
community resilience can be explained by
FIGURE 1 | Case study 1: the presence of a plant increased
theresilience of microbial community composition 77 days after
endingthe glasshouse-based drought [F(1, 24) = 15.7, P = 0.0005].
Resiliencewas greater in grassland than in wheat [F(1, 24) = 5.36,
P = 0.029]; therewere no interaction effects between land use or
previous drought. Pairwisecomparisons within land use and field
drought treatments indicated thatonly within the wheat field
drought treatment the treatments with andwithout plant were
(marginally) significantly different (Tukeys HSDcomparison, P =
0.059, indicated by an asterisk).
microbial community structure. As in case study 1, we used
PCAscores as microbial community metrics, alongside F/B ratio
andGram-positive/Gram-negative ratio.
The results from this case study support hypothesis 1. Wefound
that resilience was negatively related to the F/B ratio andthe
Gram-positive/Gram-negative ratio. In addition, resilienceincreased
with greater PC1 scores (Table 4), along which mostGram-negative
PLFAs increased and fungal PLFA decreased(Table A5). This dataset
did not allow for testing the otherhypotheses.
CASE STUDY 3: TROPICAL FOREST SOIL MICROBIALCOMMUNITIES
RESPONSES TO LITTER ADDITION, LITTERREMOVAL, AND RAINFALL EXCLUSION
IN A FIELDEXPERIMENTNemergut et al. (2010) published a study
assessing the impactof organic matter content through on soil
microbial commu-nities in Costa Rican tropical forest soils. The
design includedthree experimental treatments (litter exclusion,
litter addition,and throughfall exclusion) and one control, each
observed overtime in triplicate plots. The control plots were
sampled at thebeginning of the experiment, in April 2007, and then
subse-quently in June and October 2008. The experimental plots
weresampled in June and October 2008, resulting in 27 total
observa-tions. Pyrosequencing of the 16S rRNA gene was used to
measureof bacterial and archaeal community structure, and a suite
ofsoil environmental parameters were also assessed, including:
soilwater content, microbial biomass, CO2 efflux, dissolved
oxygen,and ammonium and nitrate concentrations. The sequencing
data,contextual data, and metadata were deposited in MG-RAST
andmade publicly available. The Nemergut et al. (2010) dataset
wasselected as a case study because parameters of interest to
globalchange disturbance were measured (microbial community
struc-ture, soil resources, and soil moisture), and because it
provideda sequence-based assessment of composition to complement
thePLFA-based assessments of Case Studies 1 and 2.
In the original work, the authors reported that certain phylaof
bacteria and archaea were more prevalent in some of theexperimental
treatments than others, and, more specifically, thatoligotrophic
taxa (e.g., Acidobacteria) were more prevalent inplots that were
compromised in organic matter availability. Toquery the dataset
specifically about community resistance andresilience, we first
calculated resistance as the Bray-Curtis simi-larity (averaged
across replicates) between the initial time point
Table 4 | Case study 2: regression models explaining variation
in microbial community resilience at day 30 after ending the
glasshouse-based
drought.
Model Intercept P Independent variablesincluded in model
Parameter value P Adj. R2
Single, linear 0.95
-
de Vries and Shade Controls on microbial community stability
(pre-disturbance) control and the post-manipulation time
pointfor each experimental treatment (April control vs. June
treat-ment). Then, we calculated resilience as the Bray-Curtis
similar-ity between the final time point and the April
pre-disturbancecontrol (April control vs. October treatment). We
used uncon-strained correspondence analysis to determine axis
scores as ametric of microbial community structure.
We found that microbial community structure (axis 1 CAscores)
explained variability in resistance across treatments (non-linear
model: resistance was explained by main and quadraticterm of axis 1
scores, adjusted R squared= 0.89, p < 0.0001 andp = 0.004,
respectively)resistance increased with axis 1 scores.The axis 1
gradient corresponded to transition from communi-ties with a high
representation of Proteobacteria-affiliated taxa(many of which can
be classified as copiotrophs) to communi-ties with a high
representation of Acidobacteria-affiliated taxa(many of which can
be classified as oligotrophs; Table A6 onlineSuppl. Data). Thus,
this result supports hypothesis 1 that resis-tance increases with
increasing abundance of oligotrophs. Axis2 CA scores and microbial
biomass did not provide explanatoryvalue for resistance. Of all the
available environmental mea-surements, only nitrate concentrations
and moisture contentexplained variability in resilience (Pearsons
correlation betweenmoisture and nitrate 0.123, P = 0.538);
resilience increasedwith nitrate availability, but decreased with
moisture content(Table 5). This suggests that nitrate availability
and moisture areimportant for resilience of microbial communities
in tropicalsoils, and supports hypothesis 3, but not hypothesis 4,
whichpose that resilience increases with nutrient and water
availability,respectively. The dataset did not allow for testing
the remaininghypotheses.
Notably, there were only small changes in community compo-sition
within treatments over time, which prompted the authorsto combine
the time points for their original analysis. This is, insome ways,
expected because spatial variability often exceeds tem-poral
variability in soil communities (Bardgett et al., 1997; Ettemaand
Wardle, 2002). However, the resistance and resilience deter-mined
by these small changes were well-explained by communitystructure,
nitrate, and water content.
SUPPORT FOR THE HYPOTHESES A FRAMEWORK FORPREDICTING MICROBIAL
COMMUNITY RESISTANCE ANDRESILIENCE TO CLIMATE CHANGEIn all three
case studies, the resistance and the resilienceof microbial
communities could be explained by community
properties associated with the r-K spectrum. We found that
themeasures that significantly explained resistance and resilience
andwere indicative for shifts from r-strategists to K-strategists
werestrongly interrelated (Tables A1A3), confirming that these
mea-sures inform about broad shifts in community structure linked
tochanges in the abundance of r- and K-strategists. Moreover,
thepresence and abundance of higher tropic levels, resource
avail-ability, and moisture content were strong predictors for
microbialcommunity resilience. Although the structure of the data
we ana-lyzed does not allow for drawing conclusions on the
relativeimportance of those controls, and the relationships we
foundare not necessarily causal, these results are a first
observationand exploration of a framework for predicting the
response ofsoil microbial communities to climate change based on
the ratiobetween r- and K-strategists, and the environment (Figure
2, toppanel). We propose that, although the underlying specific
func-tional genes present in a microbial community determine
itsresponse to climate change, simple measures that
characterizemicrobial communities along the r-K spectrum can inform
itsability to resist and recover from climate change related
distur-bances. Our framework also takes into account the effect of
theenvironment, and interrelationships between environment and r-K
dominance ofmicrobial communities, in the three-dimensionalresponse
plane.
Furthermore, we propose that the abundance of specificfunctional
genes such as those involved in desiccation resis-tance will
predict a communitys response to drought, butgenes involved in C
and N cycling might link to the r-Kspectrum and thus be useful for
predicting microbial com-munity response to climate change (Table
1; Figure 2). Forexample, the abundance of amoA genes is likely to
be greaterin N-poor environments in which the dominant N form
isammonia than in nutrient rich environments in which thedominant
form is nitrate (Schimel and Bennett, 2004), andmight thus be
associated with microbial communities dom-inated by oligotrophs.
Ultimately, our framework allows forplotting specific functional
traits onto this plane for predict-ing microbial community
stability under a range of specificdisturbances.
FUTURE DIRECTIONS: THE ROLES OF MULTIPLEDISTURBANCES AND
ADAPTATION FOR SOIL MICROBIALCOMMUNITY STABILITYBy selecting for
specific traits among community members,a disturbance may affect a
communitys ability to respond
Table 5 | Case study 3: regression models explaining variation
in microbial community resilience after litter addition, litter
removal, and rainfall
exclusion.
Model Intercept P Independent variablesincluded in model
Parameter value P Adj. R2
Single, linear 0.22
-
de Vries and Shade Controls on microbial community stability
C cycling genes
Drought resistance genes
N cycling genes
Resilient
Resistant
Dominated by K-strategists
Nutrient-poor
Nutrient-richDominated by r-
strategists Microbial community structure
Envir
onm
entR
espo
nse
Quantification of the relative abundance of functional genes
(multidimensional space)
Community response to climate change
1
2
Discovery and annotation of new gene sequences that code for
traits impor-tant for resistance or resilience
TGACTTAGTACGATCGATAGTTAGGCTAGTATAGGGTTCATGACGATCGATAGTTAGGCTAGTATAGGGTTCATGTGACTTAGTTAGTTAGGCTAGTATAGGGTTCATGTGACTTAGTACGATCGATTAGTACGATCGATAGTTAGGCTAGTATAGGGTTCATGTGACTGACTTAGTACGATTAGGCTAGTATAGGGTTCATGTCGATAGATAGGGTTTGACTTAGTACGATCGATAGTTAGGCTAGTCATG
FIGURE 2 | Framework for predicting microbial communityresponse
to climate change. The bottom part of the figureillustrates the
necessity of characterizing and annotating specificfunctional genes
(here conceptually represented by coloredsequences) that code for
microbial traits of importance forcommunity responses for specific
disturbances associated withclimate change. Once known and
annotated, these genes caninform about the relative abundance of a
suite of genes thatmay underlie a communitys response to climate
change (arrow 1).The middle part designates the relative abundance
of functionalgenes present in a community. This space is
multidimensional andhere we chose to visualize C cycling genes, N
cycling genes,and drought resistance genes (see Table 1), but other
knownand unknown genes such as those involved in sporulation
orspecific dispersal mechanisms should be included. The
functional
genes present in a community may, or may not, have arelationship
with the dominance of r- and K-strategists or with thecommunitys
environment (colored dots in middle and upper part).The role of
specific functional genes in a communitys responseand their links
with the r-K spectrum are yet to be elucidated(arrow 2). The upper
part of the figure indicates a communitysresponse to climate
change, as determined by the relativeabundance of r- and
K-strategists and the communitys environment(in this case nutrient
availability, but this can be replaced byother environmental
factors such as the abundance or richness ofhigher trophic levels).
A K-strategist dominated microbial communityin a nutrient-poor
environment likely has high resistance, whereasan r-dominated
community in a nutrient-rich environment likely hashigh resilience.
The exact shape of the surface might varydepending on specific
circumstances.
to a subsequent disturbance or to a series of
compoundeddisturbances. For example, it has been shown that the
order ofdifferent types of disturbances influences the outcome of
com-munity structure, suggesting that selection for a specific
trait
affects the ability to respond to a subsequent disturbance of
adifferent type (Fukami, 2001). Thus, we may expect that whena
microbial community is exposed to two subsequent distur-bances of
the same type, its composition will be more resistant
www.frontiersin.org September 2013 | Volume 4 | Article 265 |
9
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de Vries and Shade Controls on microbial community stability
to the second disturbance because of selection for the toler-ant
trait by the first disturbance. There is some support forthis
hypothesis from soils. Precipitation regime affected theresponse of
soil bacterial community composition to subsequentdrought and
rewetting events (Evans and Wallenstein, 2012),and extremophiles
are often tolerant to a wide range of distur-bances (Mangold et
al., 2013). In contrast, microbial commu-nities exposed to severe
drought appeared to be more resistantto a subsequent heat wave,
suggesting that the microbial traitsresponsible for drought
tolerance are related to those of heat-tolerance (Berard et al.,
2012). However, very little is knownabout the interrelatedness
between specific functional traits insoil microbes, which makes it
difficult to predict responsesto multiple disturbances. In
contrast, the r-K spectrum mightinform about a microbial communitys
ability to withstand dif-ferent types of disturbance: r-strategists
thrive in nutrient (Nand P) rich, disturbed environments compared
to K-strategists,but are less resistant to climate change than
K-strategists(Hedlund et al., 2004; De Vries et al., 2012a).
Adaptation also may be an important strategy for individ-ual
microbial taxa to cope with a changing climate (Box 1).A microbes
ability to adapt to disturbance is linked to itsgeneration time or
turnover rate, and therefore r-strategistsmay show quicker
adaptation than K-strategists. Moreover,warming can increase growth
rates, but also horizontal genetransfer between bacterial taxa
(Pritchard, 2011). In addi-tion, for example, it has been shown
that E. coli can acquirestress resistance to a range of
disturbances after pretreat-ment with a different disturbance after
only 500 generationtimes (Dragosits et al., 2013). This so called
cross-stress pro-tection has been shown for a range of species
across king-doms. Similar to microbial community resilience, rates
ofadaptation and evolution are likely influenced by environ-mental
factors such as the abundance and richness of highertrophic levels,
moisture availability, and resource availability.Although not
within the scope of this paper, these find-ings suggest that
evolutionary changes might be of equalimportance to shifts in
community structure for determiningthe response of microbial
communities to climate change(Orsini et al., 2013).
CONCLUSIONOur aim in this paper was to hypothesize controls
onmicrobial community resistance and resilience to climatechange,
and to explore our hypotheses by carefully re-analyzingthree
vignettes of published data. Our results show thatboth microbial
community properties associated with the r-K spectrum and
environmental factors such as the abun-dance and richness of higher
trophic levels, plant presence,and resource availability can
explain the response of micro-bial community structure to climate
change-related distur-bances. A clear limitation to our study is
the relativelynarrow focus on three vignettes of case studies, and
fur-ther investigation and experimental validation is necessary
todirectly test these hypotheses across a wide range of
soilecosystems. Although querying publicly available data can
beused to formulate hypotheses on the potential controls of
microbial community resistance and resilience, disentanglingthe
interwoven controls on microbial community resistanceand resilience
requires mechanistic experiments designed to testspecific questions
about the hypothesized controls (Jansson andProsser, 2013).
As a final consideration, it is possible that routine
suc-cessional trajectories of microbial communities (for
example,seasonal trajectories in temperate soils) may be altered
per-manently as a result of a disturbance. However, the nature
ofthese alterations will depend on the traits present in the
com-munity and on the type of disturbance. In temperate
aquaticsystems, it has been suggested that annual seasonal
successionin bacterial community composition may serve as a
baselinefrom which a communitys response to a pulse disturbancescan
be measured, while gradual shifts in this succession may beused as
an indicator of long-term adaptations to press distur-bances such
as global climate changes (Shade et al., 2012a,b).Similarly, soil
community successional trajectories may be quan-tified and
monitored to detect gradual shifts in compositionover the long
term, such as in response to the press distur-bance of increased
temperature, and how these shifts affectshort-term responses to
pulse disturbances, such as drought.However, typical rates of
community turnover in soil systemsare not well documented,
especially at the same site on inter-annual scales, and in the
absence of any disturbance (Shadeet al., 2013). Knowledge of these
baseline seasonal dynam-ics for soils is crucial for providing
context for communityresponses to pulse disturbances, like drought
and flooding.Therefore, collecting time series of soil communities
and quan-tifying baseline fluctuations should be prioritized toward
the goalof further understanding microbial community stability
givenongoing and compounded global climate change
disturbances.Combined with long-term experiments that directly
manipu-late anticipated global change disturbances [e.g., free-air
carbondioxide enrichment experiments (Ainsworth and Long, 2005)],we
think that these time series will provide essential insightsinto
the important microbial traits and environmental condi-tions that
may alter or maintain ecosystem services in the face ofglobal
changes.
ACKNOWLEDGMENTSWe thank the Joint EU-US Workshop Microbial
CommunityDynamics: Cooperation and Competition of the
European-United States Task Force on Biotechnology Research
forencouraging our collaboration. AS is a Gordon and BettyMoore
Foundation Fellow of the Life Sciences ResearchFoundation. We thank
Diana Nemergut for permission to re-analyze the dataset for case
study 3 from MG-RAST, andwe thank Helen Gordon for providing us
with the datasetfor case study 2. We also thank two anonymous
refer-ees and the editor for their constructive comments on
themanuscript.
SUPPLEMENTARY MATERIALThe Supplementary Material for this
article can be found onlineat:
http://www.frontiersin.org/Terrestrial_Microbiology/10.3389/fmicb.2013.00265/abstract
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2013 | Volume 4 | Article 265 | 10
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de Vries and Shade Controls on microbial community stability
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Conflict of Interest Statement: Theauthors declare that the
researchwas conducted in the absence of anycommercial or financial
relationshipsthat could be construed as a potentialconflict of
interest.
Received: 04 April 2013; accepted:17 August 2013; published
online: 05September 2013.Citation: de Vries FT and Shade A(2013)
Controls on soil microbial com-munity stability under climate
change.Front. Microbiol. 4:265. doi: 10.3389/fmicb.2013.00265This
article was submitted to TerrestrialMicrobiology, a section of the
journalFrontiers in Microbiology.Copyright 2013 de Vries and
Shade.This is an open-access article dis-tributed under the terms
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distribution or reproduction inother forums is permitted, provided
theoriginal author(s) or licensor are cred-ited and that the
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de Vries and Shade Controls on microbial community stability
APPENDIX
Table A1 | Pearson correlation coefficients between
variables
explaining microbial community resistance in Case study 1.
PC1 axis F/B ratio Gram+/Gram ratio
PC1 axis
F/B ratio 0.75
Gram+/Gram ratio 0.87 1.7 * 105
Underlines values designate significant correlations (P <
0.05).
Table A2 | Pearson correlation coefficients between
variables
explaining microbial community resilience in Case study 1.
Protozoa Micro PC1 F/B Gram+/ Microbialarthropods ratio Gram
C/N
ratio ratio
Protozoa
Microarthropods 0.35PC1 0.43 0.35F/B ratio 0.26 0.10 0.36
Gram+/Gramratio
0.08 0.015 0.43 0.67
Microbial C/Nratio
0.09 0.18 0.25 0.21 0.21
Underlines values designate significant corrections (p <
0.05).
Table A3 | Pearson correlation coefficients between
variables
explaining microbial community resilience in Case study 2.
F/B ratio PC1 Gram+/Gramratio
Microbialbiomass
F/B ratio
PC1 0.71Gram+/Gramratio
0.59 0.97
Microbialbiomass
0.63 0.90 0.92
Underlines values designate significant correlations (P <
0.05).
Table A4 | Axis loadings of individual PLFA in Case study 1.
PC1 score PC2 score
i.C14.0 0.27999 3.67E-05C14.0 0.25943 0.15196i.C15.0 0.28069
0.04368a.C15.0 0.28717 0.0606C15.0 0.01904 0.19215X3.hydroxy.C12.0
0.03272 0.17065methyl.C16.0 0.00739 0.243093C16.0 0.007681
0.201414
C16.1w7 0.024379 0.45926X10.methyl.C16.0 0.110574 0.294226
i.C17.0 0.168 0.275936
a.C17.0 0.220549 0.157182
i.C17.1w6 0.067054 0.13123n.methyl.C17.0 0.250714 0.13939C17.0
0.266306 0.05702C17.0.cyclo 0.205752 0.30061X10.methyl.C17.0
0.194742 0.16161C18.0 0.266156 0.049383
C18.1 0.058706 0.156452
trans.C18.1w9 0.241332 0.06568cis.C18.1w9 0.222525
0.07776X10.methyl.C18.0 0.267682 0.11721cis.C18.2w6 0.020817
0.373708
C18.3 0.0547 0.096218C19.0.cyclo 0.250798 0.192782
C20.0 0.251506 0.11747
Axis 1 explained 43% of variation, axis 2 explained 14% of
variation. PLFAs
marked green, red, and yellow are representative of
Gram-positive, Gram-
negative, and fungi, respectively.
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Table A5 | Axis loadings of individual PLFA in Case study 2.
PC1 score PC2 score
Methyl.2.hydroxydecanoate 0.218345 0.04861i.C14.0 0.215282
0.04387C14.0 0.11147 0.16243i.C15.0 0.21785 0.1755a.C15.0 0.241314
0.032725
C15.0 0.241365 0.052056
X14.methyl.C15.0 0.24439 0.03885X3.hydroxy.C12.0 0.23551
0.11515methyl.C16.0 0.24441 0.0385C16.0 0.16101 0.30498C16.1w7
0.222117 0.165607
C16.1 and C17.0merged 0.057127 0.32505i.C17.0 0.117704
0.11584a.C17.0 0.130821 0.029151
X2.hydroxy.C14.0 0.14187 0.12541i.C17.1w6 0.201859
0.01235n.methyl.C17.0 0.08864 0.21781C17.0 0.107442 0.153301
C17.0cyclo 0.2358 0.02104
X10.methylC17.0 0.23775 0.04534X3.OH.C14.0 0.23064 0.10203C18.0
0.02399 0.352795C18.1 0.037532 0.107055
trans.C18.1w9 0.12215 0.366805cis.C18.1w9 0.229866 0.126425
X10.methyl.C18.0 0.08328 0.358585cis.C18.2w6 0.15507
0.3251X2.hydroxy.hexadecanoic.methyl.ester 0.114723 0.02548C18.3
0.14311 0.117455C19.0.cyclo 0.2067 0.199156C20.0 0.14997
0.05973
Axis 1 explained 53% of variation, axis 2 explained 15% of
variation. PLFAs
marked green, red, and yellow are representative of
Gram-positive, Gram-
negative, and fungi, respectively.
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Table A6 | CA axis scores for the 20 most abundant bacterial
taxa in Case study 3.
OTU_ID CA1 CA2 Total no. seqs Consensus lineage
7721 0.052755783 0.128596257 1232 k__Bacteria;
p__Proteobacteria;c__Alphaproteobacteria;
o__Rhizobiales;f__Hyphomicrobiaceae; g__Rhodoplanes; s__
7592 0.277985389 0.384482383 559 k__Bacteria;
p__Proteobacteria;c__Alphaproteobacteria;
o__Rhizobiales;f__Bradyrhizobiaceae
5179 0.031267675 0.274724392 477 k__Bacteria;
p__Proteobacteria;c__Alphaproteobacteria;
o__Rhizobiales;f__Hyphomicrobiaceae; g__Rhodoplanes; s__
4450 0.368835276 0.01104972 434 k__Bacteria;
p__Proteobacteria;c__Deltaproteobacteria;
o__Syntrophobacterales;f__Syntrophobacteraceae; g__; s__
3664 0.203535915 0.204631553 397 k__Bacteria;
p__Proteobacteria;c__Alphaproteobacteria;
o__Rhizobiales;f__Hyphomicrobiaceae; g__Rhodoplanes; s__
232 0.317417102 0.327066878 291 k__Bacteria;
p__Acidobacteria;c__Acidobacteria-5; o__; f__; g__; s__
6514 0.374990616 0.045919714 275 k__Bacteria;
p__Acidobacteria;c__Acidobacteria-2; o__; f__; g__; s__
3615 0.148161131 0.186057802 271 k__Bacteria;
p__Acidobacteria;c__Acidobacteria-2; o__; f__; g__; s__
6194 0.272492541 0.440098295 243 k__Bacteria; p__Nitrospirae;
c__Nitrospira;o__Nitrospirales; f__Nitrospiraceae;g__Nitrospira;
s__
1968 0.406077387 0.153510689 236 k__Bacteria;
p__Proteobacteria;c__Alphaproteobacteria;
o__Rhizobiales;f__Hyphomicrobiaceae; g__Rhodoplanes; s__
2877 0.452435542 0.331937119 214 k__Bacteria; p__Bacteroidetes;
c__Flavobacteriia;o__Flavobacteriales;
f__Flavobacteriaceae;g__Flavobacterium
5980 1.037736522 0.623269339 211 k__Bacteria;
p__Acidobacteria;c__Acidobacteria-2; o__; f__; g__; s__
3158 0.511248597 0.360815747 177 k__Bacteria;
p__Proteobacteria;c__Betaproteobacteria; o__; f__; g__; s__
741 0.561319057 0.242946783 166 k__Bacteria; p__Acidobacteria;
c__Acidobacteria;o__Acidobacteriales; f__Koribacteraceae; g__;
s__
9410 0.330214073 0.053950661 162 k__Bacteria; p__Acidobacteria;
c__Acidobacteria;o__Acidobacteriales;
f__Koribacteraceae;g__Candidatus Koribacter; s__
4283 0.21498302 0.197813008 158 k__Bacteria;
p__Proteobacteria;c__Alphaproteobacteria;
o__Rhizobiales;f__Rhodobiaceae; g__; s__
2587 0.012292612 0.229939177 157 k__Bacteria;
p__Proteobacteria;c__Alphaproteobacteria;
o__Rhizobiales;f__Hyphomicrobiaceae; g__Rhodoplanes; s__
2250 0.126551331 0.137573106 154 k__Bacteria;
p__Proteobacteria;c__Deltaproteobacteria;
o__Syntrophobacterales;f__Syntrophobacteraceae; g__; s__
8618 0.03958676 0.222012965 153 k__Bacteria;
p__Proteobacteria;c__Gammaproteobacteria;
o__Xanthomonadales;f__Sinobacteraceae; g__; s__
CA axis 1 explained 5.47% and CA axis 2 explained 5.05% of
variation.
Frontiers in Microbiology | Terrestrial Microbiology September
2013 | Volume 4 | Article 265 | 16
Controls on soil microbial community stability under climate
changeIntroductionMicrobial Community Structure, Specific Traits
Present in a Community, and the R-K SpectrumHypothesis 1: The
Resistance of Microbial Community Structure to Disturbance
Increases with Increasing Relative Abundance of K Strategists (or
Oligotrophs), but the Resilience Decreases.
Higher Trophic LevelsHypothesis 2: the resilience of Microbial
Community Structure Increases with Greater Diversity of Organisms
of Higher Trophic Levels
Resource AvailabilityHypothesis 3: the Resilience of Microbial
Community Structure Increases with Greater Resource Availability.
Because of the Belowground C Inputs by Plant, the Presence of a
Plant will Increase the Resilience of the Microbial Community
Moisture AvailabilityHypothesis 4: Moisture Availability
Increases Resilience of Microbial Community Structure
MethodsCase study 1: Responses of Grassland and Wheat Field
Microbial Communities to Multiple Drought EventsCase Study 2:
Response of Microbial Communities from Intensively Managed and
Extensively Managed Grassland to DroughtCase Study 3: Tropical
Forest Soil Microbial Communities Responses to Litter Addition,
Litter Removal, and Rainfall Exclusion in a Field ExperimentSupport
for the Hypotheses - a Framework for Predicting Microbial Community
Resistance and Resilience to Climate ChangeFuture Directions: The
Roles of Multiple Disturbances and Adaptation for Soil Microbial
Community StabilityConclusionAcknowledgmentsSupplementary
MaterialReferencesAppendix